Visuals_Draft_04_14

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trying URL 'http://cran.us.r-project.org/bin/macosx/contrib/4.0/rgeos_0.5-5.tgz'
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    ADM_RATE < 0.2 ~ 'elite/highly selective',

Error: unexpected ',' in "    ADM_RATE < 0.2 ~ 'elite/highly selective',"

Simple Scattergram

First we are going to try to present this pattern for different tiers of universities (admission rate as well as debt)

This is a data table showing the breakdown of the university ranks (as I chose to rank them). I will note that from the dt alone wwe are seeing a downward trend in the ‘Median Student Loans’ column.

# Create Data Table (Summarized) for 
library('scales')

sc_dt <- sc %>% subset(DEBT_MDN !='PrivacySuppressed') %>% transform(DEBT_MDN = as.numeric(DEBT_MDN)) %>% group_by(uni_rank) %>% mutate(`Number of Universities` = n()) %>% ungroup() %>% mutate(DEBT_MDN_STUDENTS = DEBT_MDN*UGDS) %>% group_by(uni_rank) %>% mutate(`Median Student Loans` = paste('$',round(sum(DEBT_MDN_STUDENTS, na.rm=TRUE)/sum(UGDS, na.rm=TRUE),2))) %>% 
  mutate(`Min Acceptance Rate` = percent(min(ADM_RATE))) %>% mutate(`Max Acceptance Rate` = percent(max(ADM_RATE))) %>% ungroup() %>% 
  group_by(uni_rank,`Median Student Loans`,`Number of Universities`,`Min Acceptance Rate`,`Max Acceptance Rate`) %>% 
  summarize()

`summarise()` regrouping output by 'uni_rank', 'Median Student Loans', 'Number of Universities', 'Min Acceptance Rate' (override with `.groups` argument)

install.packages('DT')

Error in install.packages : Updating loaded packages

library(DT)
table <- datatable(sc_dt,style = "default",filter = 'top',  caption = 'Universities and Selectivity')
table

NA

Scattergram as Violin Plot

Showing the previous scattergram specifically as violing plots

ipeds15 <- get_education_data(level = "college-university",
    source = "ipeds",
    topic = "grad-rates-pell",
    filters = list(year = 2015))
ipeds15

Student Debt over Time

#Process the data 
sc_time <- read_csv('2010_2019_student_debt.csv') 

Error: '2010_2019_student_debt.csv' does not exist in current working directory ('/Users/ConnieXu/Dropbox (Business)/Spring 2021/QMSS 5063 - Data Visualization /Group_G_HigherEd/ideas_drafts').

sum(sc_time$UGDS,na.rm=TRUE)

[1] 90801007

Below is an interactive line graph (2010-2019) which details the trends in student debt over the years.

# CPI Inflation Rates - Got Average Yearly Inflation Rate for Scaling for Student Debt 
install.packages('quantmod')

Error in install.packages : Updating loaded packages

library(quantmod)
getSymbols("CPIAUCSL", src='FRED')

[1] "CPIAUCSL"

avg.cpi <- apply.yearly(CPIAUCSL, mean)
cf <- as.data.frame(avg.cpi/as.numeric(avg.cpi['2009'])) 
cf$Year_Ending <- format(as.Date(row.names(cf), format="%Y-%m-%d"),"%Y")

# Merged for Inflation 
sc_time_df <- sc_time %>% group_by(`Year_Ending`) %>% mutate(`Average Annual Student Debt - National` = sum(DEBT_MDN_STUDENT,na.rm=TRUE)/sum(UGDS,na.rm=TRUE)) %>% ungroup() %>% 
  dplyr::mutate(uni_rank = case_when(
    ADM_RATE < 0.2 ~ 'elite/highly selective',
    ADM_RATE < 0.3 ~ 'more selective',
    ADM_RATE < 0.5 ~ 'selective',
    ADM_RATE < 0.7 ~ 'less selective',
    TRUE ~ 'not selective')) %>%
  mutate(uni_rank = factor(uni_rank, levels=c('not selective', 'less selective', 'selective', 
                                              'more selective', 'elite/highly selective'))) %>%
  group_by(uni_rank,Year_Ending) %>% 
  mutate(`Average Annual Student Debt (by Selectivity)` = sum(DEBT_MDN_STUDENT,na.rm=TRUE)/sum(UGDS,na.rm=TRUE)) %>% 
  ungroup() %>% 
  group_by(`Year_Ending`,`Average Annual Student Debt (by Selectivity)`,
           uni_rank,`Average Annual Student Debt - National`) %>% summarize() %>% 
  merge(cf) %>% 
  mutate(`Adjusted Average Annual Student Debt` = `Average Annual Student Debt (by Selectivity)`/
           CPIAUCSL) %>% 
  mutate(`Adjusted Average Annual Student Debt - Composite` = `Average Annual Student Debt - National`/
           CPIAUCSL)

`summarise()` regrouping output by 'Year_Ending', 'Average Annual Student Debt (by Selectivity)', 'uni_rank' (override with `.groups` argument)

sc_df <- sc_time_df %>% group_by(`Average Annual Student Debt - National`,`Adjusted Average Annual Student Debt - Composite`,Year_Ending) %>% summarize() %>% mutate(uni_rank='national average') %>% mutate(`Adjusted Average Annual Student Debt`=`Adjusted Average Annual Student Debt - Composite`) %>% dplyr::mutate(`Average Annual Student Debt (by Selectivity)` = `Average Annual Student Debt - National`) %>% merge(cf) %>% select(Year_Ending,`Average Annual Student Debt (by Selectivity)`, uni_rank, `Average Annual Student Debt - National`, CPIAUCSL, `Adjusted Average Annual Student Debt`,`Adjusted Average Annual Student Debt - Composite`)

`summarise()` regrouping output by 'Average Annual Student Debt - National', 'Adjusted Average Annual Student Debt - Composite' (override with `.groups` argument)

sc_time_df <- sc_time_df %>% rbind(sc_df) %>% mutate(uni_rank = factor(uni_rank, levels=c('national average','not selective', 'less selective', 'selective', 'more selective', 'elite/highly selective'))) %>% 
  mutate(national = ifelse(uni_rank == 'national average', 'y','n'))
sc_df

sc_time_df

brewer.pal(n=10,"PuBuGn")

n too large, allowed maximum for palette PuBuGn is 9
Returning the palette you asked for with that many colors

[1] "#FFF7FB" "#ECE2F0" "#D0D1E6" "#A6BDDB" "#67A9CF" "#3690C0" "#02818A" "#016C59" "#014636"

ShortPuBuGn <- c("#D0D1E6","#A6BDDB","#67A9CF","#3690C0","#02818A")

p <- sc_time_df %>% 
  ggplot(.,aes(x=Year_Ending,y=`Adjusted Average Annual Student Debt`, color=uni_rank, group=national)) + 
  geom_point() + geom_line(aes(linetype=national)) + 
  scale_color_manual(values=c('grey',"#D0D1E6","#A6BDDB","#67A9CF","#3690C0","#02818A"))+
  theme(
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.background= element_rect(fill="white")) +
  scale_x_continuous(breaks = round(seq(min(sc_time$Year_Ending), max(sc_time$Year_Ending), by = 2),1)) +
  labs(x='', y='Median Loan Amount per Student\n(thousands)**', 
       title='Student Debt Has Been Rising Over The Years',
       color='',fill='', caption='**-inflation adjusted')
ggplotly(p)

NA
NA

The following is (instead) a bar graph with fewer ‘university selectivity’ buckets.

Chloropleth showing average student debt (this is only 2019 but I have a filter at the beginning that will allow me to incorporate a slider for the year).

leaflet(states_2019) %>% addProviderTiles("CartoDB.Positron") %>%
  addPolygons(fillColor = ~pal(states_2019$`Average Student Loans`),
              color = "white",
              weight = 0.5,
              fillOpacity = 0.7,  
              highlight = highlightOptions(
                weight = 5,
                color = "#666",
                fillOpacity = 0.7,
                bringToFront = TRUE,
                ),popup=pop_pop) %>%
  leaflet::addLegend(position = "bottomleft", pal = pal, values = c(paste('$',round(min(states_2019$`Average Student Loans`))), 
                                                          paste('$',round(max(states_2019$`Average Student Loans`)))),
            title = "Average Student Loans (Per Student)") %>%   setView(-98.5795, 39.8282, zoom=3)

Some values were outside the color scale and will be treated as NAsf layer has inconsistent datum (+proj=longlat +datum=NAD83 +no_defs).
Need '+proj=longlat +datum=WGS84'

brewer.pal(n = 8, name = "RdYlGn")

leaflet(sc_time_2019_selective) %>% addTiles('http://{s}.basemaps.cartocdn.com/dark_all/{z}/{x}/{y}.png') %>%
  addCircles(col = ~pal1(sc_time_2019_selective$uni_rank),
             radius = ~DEBT_MDN,
             popup = content,
             fillOpacity = 0.7) %>%
  leaflet::addLegend(position = "bottomleft",pal = pal1, values = sc_time_2019_selective$uni_rank,
            title = "Average Student Loans (Per Student)")

Assuming "long" and "lat" are longitude and latitude, respectively
Data contains 931 rows with either missing or invalid lat/lon values and will be ignored

The code chunk below has not been touched and includes Shiny code for later use.

ui <- fluidPage(
    sidebarLayout(
        sidebarPanel(
            p
        ),
        
        mainPanel(
            width = 9,
            h2("Link to a saved sample.html"),
            p("The url is https://johndoe.github.io/samples/sample.html"),
            a(h3("Lovely Birds"),
              href = "https://johndoe.github.io/samples/sample.html",
              target = "blank"),
            hr(),
            h2("Text and image example", id = "birds"),
            fluidRow(
                column(
                    7,
                    includeMarkdown("markdown/birds.Rmd")
                ),
                column(
                    5,
                    img(src = "birds.png", width = "100%")
                )
            ),
        )
    )
)

cannot open file 'markdown/birds.Rmd': No such file or directoryError in file(con, "r") : cannot open the connection